W1: Vectors, data.frames and lists

Welcome!

Introductions

  • Who am I?
  • TA: Monica Gerber - in-class resource
  • What is DaSL?

  • Who are you?

    • Name, pronouns, group you work in
    • What you want to get out of the class
    • Favorite spring activity

Goals of the course

  • Continue building programming fundamentals: How to use complex data structures, use and create custom functions, and how to iterate repeated tasks using tools in the tidyverse.
  • Continue exploration of data science fundamentals: How to clean messy data to a Tidy form for analysis using tools in the tidyverse.

  • At the end of the course, you will be able to: conduct a full analysis in the data science workflow (minus model).

    Data science workflow

Culture of the course

  • Learning on the job is challenging
    • I will move at learner’s pace; we are learning together.
    • Teach not for mastery, but teach for empowerment to learn effectively.
  • Various personal goals and applications: curate applications based on your interest!
  • Respect Code of Conduct

Format of the course

. . . - 6 classes: Jan 22, 29, Feb. 5, 12, 26, Mar 6 - No class during Public School Week

  • Streamed online and in person, recordings will be available.

  • Announcements via Teams Classroom and by Google Doc . . .

  • 1-2 hour exercises after each session are strongly encouraged as they provide practice.

  • Optional time to work on exercises together on Fridays Noon - 1pm PT.

  • Online discussion via Slack.

Content of the course

Week Date Subject
1 Jan 22* Fundamentals: vectors, data.frames, and lists
2 Jan 29 Data Cleaning 1
3 Feb 5 Data Cleaning 2
4 Feb 12* Writing Functions
- Feb 19 No class - school week
5 Feb 26* Iterating/Repeating Tasks
6 Mar 6* Overflow/Celebratory Lunch

*Ted on Campus

Office Hours

  • Opportunity to Practice & ask questions
  • 10 - 11 AM PST Fridays
  • Outlook link will be shared

Ask me two questions

Set up Posit Cloud and look at our workspace!

Break

A pre-course survey:

https://forms.gle/aLXyQor4WS5mTKMV6

Note

  • We’ll do exercises live in the slides
  • they are mirrored in your workspaces as classwork
  • Exercises in your projects

Data types in R

  • Numeric: 18, -21, 65, 1.25
  • Character: “ATCG”, “Whatever”, “948-293-0000”
  • Logical: TRUE, FALSE
  • Missing values: NA

Data structures in R

  • Vector
  • Dataframe
  • List

Vectors

A vector contains a data type, and all elements must be the same data type. We can have logical vectors, numerical vectors, etc.

Within the Numeric type that we are familiar with, there are more specific types: Integer vectors consists of whole number values, and Double vectors consists of decimal values.

fib = c(0, 1, 1, NA, 5)

Testing and Coercing

We can test whether a vector is a certain type with is.___() functions, such as is.character().

is.character(c("hello", "there"))
[1] TRUE

For NA, the test will return a vector testing each element, because NA can be mixed into other values:

is.na(c(34, NA))
[1] FALSE  TRUE

We can coerce vectors from one type to the other with as.___() functions, such as as.numeric()

as.numeric(c("23", "45"))
[1] 23 45
as.numeric(c(TRUE, FALSE))
[1] 1 0

Attributes of data structures

It is common to have metadata attributes, such as names, attached to R data structures.

x = c(1, 2, 3)
names(x) = c("a", "b", "c")
x
a b c 
1 2 3 

x["a"]
a 
1

We can look for more general attributes via the attributes() function:

attributes(x)
$names
[1] "a" "b" "c"

Ways to subset a vector

data = c(2, 4, -1, -3, 2, -1, 10)

  1. Positive numeric vector

    data[c(1, 2, 7)]
    [1]  2  4 10

  1. Negative numeric vector performs exclusion

    data[-1]
    [1]  4 -1 -3  2 -1 10
  1. Logical vector
data[c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE)]
[1]  2  4 10

Comparison operators, such as >, <=, ==, !=, create logical vectors for subsetting.

data < 0
[1] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
data[data < 0]
[1] -1 -3 -1

Try it Out: Vectors 1

  1. How do you subset the following vector so that it only has positive values?
data = c(2, 4, -1, -3, 2, -1, 10) data[data > 0] 
data = c(2, 4, -1, -3, 2, -1, 10)
data[data > 0]

Vectors 2

  1. How do you subset the following vector so that it has doesn’t have the character “temp”?
chars = c("temp", "object", "temp", "wish", "bumblebee", "temp") chars[chars != "temp"] 
chars = c("temp", "object", "temp", "wish", "bumblebee", "temp")
chars[chars != "temp"]

Vectors 3

  1. Challenge: How do you subset the following vector so that it has no NA values?
vec_with_NA = c(2, 4, NA, NA, 3, NA) vec_with_NA[!is.na(vec_with_NA)] 
vec_with_NA = c(2, 4, NA, NA, 3, NA)
vec_with_NA[!is.na(vec_with_NA)]

Dataframes

Usually, we load in a dataframe from a spreadsheet or a package.

library(tidyverse)
library(palmerpenguins)
head(penguins)

Let’s take a look at a dataframe’s attributes.

attributes(penguins)
$class
[1] "tbl_df"     "tbl"        "data.frame"

$row.names
  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
 [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
 [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
 [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
 [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
 [91]  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
[109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
[127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
[145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
[163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
[181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
[199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
[217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
[235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
[253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
[271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
[289] 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
[307] 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324
[325] 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342
[343] 343 344

$names
[1] "species"           "island"            "bill_length_mm"   
[4] "bill_depth_mm"     "flipper_length_mm" "body_mass_g"      
[7] "sex"               "year"

So, we can access the column names of the dataframe via names():

names(penguins)
[1] "species"           "island"            "bill_length_mm"   
[4] "bill_depth_mm"     "flipper_length_mm" "body_mass_g"      
[7] "sex"               "year"

Try it out: Subsetting dataframes 1

Subset to the single column bill_length_mm:

penguins$bill_length_mm # or penguins[["bill_length_mm"]] 
penguins$bill_length_mm
# or
penguins[["bill_length_mm"]]

Subsetting dataframes 2

I want to select columns bill_length_mm, bill_depth_mm, species, and filter the rows so that species only has “Gentoo”:

penguins |> select(bill_length_mm, bill_depth_mm, species) |> filter(species == "Gentoo") 
penguins |>
  select(bill_length_mm, bill_depth_mm, species) |>
  filter(species == "Gentoo")

Subsetting dataframes 3

Challenge: I want to filter out rows that have NAs in the column bill_length_mm:

penguins |> filter(!is.na(bill_length_mm)) 
penguins |>
  filter(!is.na(bill_length_mm))

Lists

Lists operate similarly as vectors as they group data into one dimension, but each element of a list can be any data type or data structure!

l1 = list(
  1:3, 
  "a", 
  c(TRUE, FALSE, TRUE), 
  c(2.3, 5.9)
)

Unlike vectors, you access the elements of a list via the double bracket [[]]. (You will access a smaller list with single bracket [].)

l1[[1]]
[1] 1 2 3
l1[[1]][2]
[1] 2

List names

We can give names to lists:

l1 = list(
  ranking = 1:3, 
  name = "a", 
  success =  c(TRUE, FALSE, TRUE), 
  score = c(2.3, 5.9)
)
#or
names(l1) = c("ranking", "name", "success", "score")

Accessing List elements

And access named elements of lists via the [[]] or $ operation:

l1[["score"]]
[1] 2.3 5.9
l1$score
[1] 2.3 5.9

Therefore, l1$score is the same as l1[[4]] and is the same as l1[["score"]].

What data structure does this remind you of?

Warning: [] versus [[]]

This always trips me up, you usually want [[]] (return an element) versus [] (returns a sublist).

l1["ranking"]
$ranking
[1] 1 2 3
l1[["ranking"]]
[1] 1 2 3

Two main uses for Lists

  1. Return a mixed type list of objects, such as from running lm() - a lot of methods in R use this.
  • Useful when programming functions
  1. Store multiple instances of the same data type, such as a list of data.frames
  • Iteration over lists

Try it Out:

Return the element in the id slot:

person = list(id=100031, age=40) person$id person[["id"]] 
person = list(id=100031, age=40)
person$id
person[["id"]]

Return the 2nd element of this list:

new_list <- list(c(1,2,3), c(3,4,5), c(5,7,8)) new_list[[2]] 
new_list <- list(c(1,2,3), c(3,4,5), c(5,7,8))
new_list[[2]]

Dataframes as Lists

A dataframe is just a named list of vectors of same length with attributes of (column) names and row.names, so all of the list methods we looked at above apply.

head(penguins)
head(penguins[[3]])
[1] 39.1 39.5 40.3   NA 36.7 39.3
head(penguins$bill_length_mm)
[1] 39.1 39.5 40.3   NA 36.7 39.3
head(penguins[["bill_length_mm"]])
[1] 39.1 39.5 40.3   NA 36.7 39.3

Everything in R is a List, or based on one

Tidyverse tools for lists

  • lapply() function - applies a function to each element of a list
  • We’ll explore in Week 5 the {purrr} package, which has methods for working with lists

That’s all!

Maybe see you Friday 10 - 11 AM PST to practice together!